{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('./train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = data.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = data[['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = pd.get_dummies(x,columns=['Pclass','Sex','Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x['Age'] = x.Age.fillna(x.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "del x[\"Sex_female\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add(layers.Dense(1,input_dim=11,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "891/891 [==============================] - 0s 385us/step - loss: 1.3557 - acc: 0.6655\n",
      "Epoch 2/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 1.0881 - acc: 0.6835\n",
      "Epoch 3/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.9492 - acc: 0.6779\n",
      "Epoch 4/300\n",
      "891/891 [==============================] - 0s 35us/step - loss: 0.8616 - acc: 0.6756\n",
      "Epoch 5/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.7954 - acc: 0.6846\n",
      "Epoch 6/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.7320 - acc: 0.6947\n",
      "Epoch 7/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.6809 - acc: 0.6947\n",
      "Epoch 8/300\n",
      "891/891 [==============================] - 0s 36us/step - loss: 0.6410 - acc: 0.7071\n",
      "Epoch 9/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.6167 - acc: 0.7037\n",
      "Epoch 10/300\n",
      "891/891 [==============================] - 0s 35us/step - loss: 0.5991 - acc: 0.7059\n",
      "Epoch 11/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.5868 - acc: 0.7082\n",
      "Epoch 12/300\n",
      "891/891 [==============================] - 0s 36us/step - loss: 0.5815 - acc: 0.7082\n",
      "Epoch 13/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.5749 - acc: 0.7160\n",
      "Epoch 14/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.5698 - acc: 0.7048\n",
      "Epoch 15/300\n",
      "891/891 [==============================] - 0s 38us/step - loss: 0.5663 - acc: 0.7048\n",
      "Epoch 16/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.5618 - acc: 0.7172\n",
      "Epoch 17/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.5586 - acc: 0.7172\n",
      "Epoch 18/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.5545 - acc: 0.7183\n",
      "Epoch 19/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.5537 - acc: 0.7194\n",
      "Epoch 20/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5491 - acc: 0.7183\n",
      "Epoch 21/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.5460 - acc: 0.7194\n",
      "Epoch 22/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.5436 - acc: 0.7239\n",
      "Epoch 23/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.5406 - acc: 0.7250\n",
      "Epoch 24/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5383 - acc: 0.7250\n",
      "Epoch 25/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5372 - acc: 0.7273\n",
      "Epoch 26/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.5348 - acc: 0.7340\n",
      "Epoch 27/300\n",
      "891/891 [==============================] - 0s 38us/step - loss: 0.5306 - acc: 0.7284\n",
      "Epoch 28/300\n",
      "891/891 [==============================] - 0s 37us/step - loss: 0.5287 - acc: 0.7419\n",
      "Epoch 29/300\n",
      "891/891 [==============================] - 0s 38us/step - loss: 0.5274 - acc: 0.7295\n",
      "Epoch 30/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.5249 - acc: 0.7407\n",
      "Epoch 31/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.5244 - acc: 0.7419\n",
      "Epoch 32/300\n",
      "891/891 [==============================] - 0s 36us/step - loss: 0.5216 - acc: 0.7407\n",
      "Epoch 33/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.5200 - acc: 0.7497\n",
      "Epoch 34/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.5172 - acc: 0.7452\n",
      "Epoch 35/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5168 - acc: 0.7520\n",
      "Epoch 36/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5135 - acc: 0.7520\n",
      "Epoch 37/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.5134 - acc: 0.7486\n",
      "Epoch 38/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.5122 - acc: 0.7654\n",
      "Epoch 39/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.5094 - acc: 0.7565\n",
      "Epoch 40/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5083 - acc: 0.7598\n",
      "Epoch 41/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.5061 - acc: 0.7609\n",
      "Epoch 42/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.5056 - acc: 0.7677\n",
      "Epoch 43/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5037 - acc: 0.7688\n",
      "Epoch 44/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.5023 - acc: 0.7778\n",
      "Epoch 45/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5033 - acc: 0.7722\n",
      "Epoch 46/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5015 - acc: 0.7755\n",
      "Epoch 47/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.5003 - acc: 0.7767\n",
      "Epoch 48/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4979 - acc: 0.7789\n",
      "Epoch 49/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4965 - acc: 0.7811\n",
      "Epoch 50/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.4965 - acc: 0.7800\n",
      "Epoch 51/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.4945 - acc: 0.7811\n",
      "Epoch 52/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4948 - acc: 0.7856\n",
      "Epoch 53/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4922 - acc: 0.7811\n",
      "Epoch 54/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4917 - acc: 0.7823\n",
      "Epoch 55/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.4907 - acc: 0.7845\n",
      "Epoch 56/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4901 - acc: 0.7811\n",
      "Epoch 57/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4904 - acc: 0.7789\n",
      "Epoch 58/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4891 - acc: 0.7845\n",
      "Epoch 59/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4872 - acc: 0.7811\n",
      "Epoch 60/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4855 - acc: 0.7845\n",
      "Epoch 61/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.4860 - acc: 0.7901\n",
      "Epoch 62/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4866 - acc: 0.7755\n",
      "Epoch 63/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.4846 - acc: 0.7890\n",
      "Epoch 64/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4845 - acc: 0.7879\n",
      "Epoch 65/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4832 - acc: 0.7868\n",
      "Epoch 66/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.4818 - acc: 0.7901\n",
      "Epoch 67/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4813 - acc: 0.7868\n",
      "Epoch 68/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.4813 - acc: 0.7935\n",
      "Epoch 69/300\n",
      "891/891 [==============================] - 0s 36us/step - loss: 0.4814 - acc: 0.7890\n",
      "Epoch 70/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4792 - acc: 0.7890\n",
      "Epoch 71/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4786 - acc: 0.7890\n",
      "Epoch 72/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4777 - acc: 0.7890\n",
      "Epoch 73/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4769 - acc: 0.7957\n",
      "Epoch 74/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4762 - acc: 0.7856\n",
      "Epoch 75/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.4750 - acc: 0.7890\n",
      "Epoch 76/300\n",
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      "Epoch 77/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4762 - acc: 0.7980\n",
      "Epoch 78/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4737 - acc: 0.7946\n",
      "Epoch 79/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4741 - acc: 0.7969\n",
      "Epoch 80/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4729 - acc: 0.8002\n",
      "Epoch 81/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4724 - acc: 0.7980\n",
      "Epoch 82/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4728 - acc: 0.8036\n",
      "Epoch 83/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4709 - acc: 0.7946\n",
      "Epoch 84/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891/891 [==============================] - 0s 28us/step - loss: 0.4705 - acc: 0.8036\n",
      "Epoch 85/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.4712 - acc: 0.7980\n",
      "Epoch 86/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.4694 - acc: 0.8025\n",
      "Epoch 87/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.4732 - acc: 0.8036\n",
      "Epoch 88/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4705 - acc: 0.8058\n",
      "Epoch 89/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4695 - acc: 0.7980\n",
      "Epoch 90/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4693 - acc: 0.8058\n",
      "Epoch 91/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4677 - acc: 0.8036\n",
      "Epoch 92/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.4673 - acc: 0.8092\n",
      "Epoch 93/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4664 - acc: 0.8070\n",
      "Epoch 94/300\n",
      "891/891 [==============================] - 0s 25us/step - loss: 0.4655 - acc: 0.8036\n",
      "Epoch 95/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4655 - acc: 0.8058\n",
      "Epoch 96/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4662 - acc: 0.8058\n",
      "Epoch 97/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4651 - acc: 0.8092\n",
      "Epoch 98/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4649 - acc: 0.8025\n",
      "Epoch 99/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4638 - acc: 0.8058\n",
      "Epoch 100/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4634 - acc: 0.8103\n",
      "Epoch 101/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4631 - acc: 0.8058\n",
      "Epoch 102/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4623 - acc: 0.8081\n",
      "Epoch 103/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4626 - acc: 0.8070\n",
      "Epoch 104/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4629 - acc: 0.8058\n",
      "Epoch 105/300\n",
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      "Epoch 106/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4622 - acc: 0.8103\n",
      "Epoch 107/300\n",
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      "Epoch 108/300\n",
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      "Epoch 110/300\n",
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      "Epoch 111/300\n",
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      "Epoch 112/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4600 - acc: 0.8114\n",
      "Epoch 113/300\n",
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      "Epoch 114/300\n",
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      "Epoch 115/300\n",
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      "Epoch 116/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4589 - acc: 0.8081\n",
      "Epoch 117/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4584 - acc: 0.8126\n",
      "Epoch 118/300\n",
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      "Epoch 119/300\n",
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      "Epoch 120/300\n",
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      "Epoch 121/300\n",
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      "Epoch 122/300\n",
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      "Epoch 123/300\n",
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      "Epoch 124/300\n",
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      "Epoch 125/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.4555 - acc: 0.8171\n",
      "Epoch 126/300\n",
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      "Epoch 127/300\n",
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      "Epoch 128/300\n",
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      "Epoch 129/300\n",
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      "Epoch 130/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4576 - acc: 0.8114\n",
      "Epoch 131/300\n",
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      "Epoch 132/300\n",
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      "Epoch 133/300\n",
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      "Epoch 134/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4542 - acc: 0.8092\n",
      "Epoch 135/300\n",
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      "Epoch 136/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4537 - acc: 0.8204\n",
      "Epoch 137/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.4542 - acc: 0.8025\n",
      "Epoch 138/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4545 - acc: 0.8148\n",
      "Epoch 139/300\n",
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      "Epoch 140/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4522 - acc: 0.8159\n",
      "Epoch 141/300\n",
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      "Epoch 142/300\n",
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      "Epoch 143/300\n",
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      "Epoch 144/300\n",
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      "Epoch 145/300\n",
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      "Epoch 146/300\n",
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      "Epoch 147/300\n",
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      "Epoch 148/300\n",
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      "Epoch 149/300\n",
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      "Epoch 150/300\n",
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      "Epoch 151/300\n",
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      "Epoch 152/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4516 - acc: 0.8126\n",
      "Epoch 153/300\n",
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      "Epoch 154/300\n",
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      "Epoch 157/300\n",
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      "Epoch 158/300\n",
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      "Epoch 159/300\n",
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      "Epoch 160/300\n",
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      "Epoch 161/300\n",
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      "Epoch 162/300\n",
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      "Epoch 163/300\n",
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      "Epoch 164/300\n",
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      "Epoch 165/300\n",
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      "Epoch 166/300\n",
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      "Epoch 167/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891/891 [==============================] - 0s 27us/step - loss: 0.4502 - acc: 0.8103\n",
      "Epoch 168/300\n",
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      "Epoch 201/300\n",
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      "Epoch 206/300\n",
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      "Epoch 207/300\n",
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      "Epoch 208/300\n",
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      "Epoch 209/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4459 - acc: 0.8070\n",
      "Epoch 210/300\n",
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      "Epoch 211/300\n",
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      "Epoch 212/300\n",
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      "Epoch 213/300\n",
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      "Epoch 214/300\n",
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      "Epoch 215/300\n",
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      "Epoch 217/300\n",
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      "Epoch 219/300\n",
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      "Epoch 223/300\n",
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      "Epoch 224/300\n",
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      "Epoch 225/300\n",
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      "Epoch 226/300\n",
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      "Epoch 231/300\n",
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      "Epoch 232/300\n",
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      "Epoch 233/300\n",
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      "Epoch 234/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4451 - acc: 0.8114\n",
      "Epoch 235/300\n",
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      "Epoch 236/300\n",
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      "Epoch 237/300\n",
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      "Epoch 238/300\n",
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      "Epoch 240/300\n",
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      "Epoch 241/300\n",
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      "Epoch 242/300\n",
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      "Epoch 244/300\n",
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      "Epoch 245/300\n",
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      "Epoch 246/300\n",
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      "Epoch 247/300\n",
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      "Epoch 248/300\n",
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      "Epoch 249/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891/891 [==============================] - 0s 27us/step - loss: 0.4437 - acc: 0.8047\n",
      "Epoch 250/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4446 - acc: 0.8036\n",
      "Epoch 251/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4437 - acc: 0.8058\n",
      "Epoch 252/300\n",
      "891/891 [==============================] - 0s 29us/step - loss: 0.4442 - acc: 0.8025\n",
      "Epoch 253/300\n",
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      "Epoch 254/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4430 - acc: 0.8070\n",
      "Epoch 255/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4435 - acc: 0.8058\n",
      "Epoch 256/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4435 - acc: 0.8092\n",
      "Epoch 257/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4436 - acc: 0.8081\n",
      "Epoch 258/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4436 - acc: 0.8047\n",
      "Epoch 259/300\n",
      "891/891 [==============================] - 0s 25us/step - loss: 0.4431 - acc: 0.8081\n",
      "Epoch 260/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.4426 - acc: 0.8047\n",
      "Epoch 261/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4433 - acc: 0.8025\n",
      "Epoch 262/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4434 - acc: 0.8081\n",
      "Epoch 263/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4437 - acc: 0.8092\n",
      "Epoch 264/300\n",
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      "Epoch 265/300\n",
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      "Epoch 266/300\n",
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      "Epoch 267/300\n",
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      "Epoch 268/300\n",
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      "Epoch 269/300\n",
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      "Epoch 270/300\n",
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      "Epoch 271/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4431 - acc: 0.8025\n",
      "Epoch 272/300\n",
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      "Epoch 273/300\n",
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      "Epoch 274/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4443 - acc: 0.8070\n",
      "Epoch 275/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4451 - acc: 0.8013\n",
      "Epoch 276/300\n",
      "891/891 [==============================] - ETA: 0s - loss: 0.3790 - acc: 0.843 - 0s 32us/step - loss: 0.4454 - acc: 0.8036\n",
      "Epoch 277/300\n",
      "891/891 [==============================] - 0s 37us/step - loss: 0.4458 - acc: 0.8114\n",
      "Epoch 278/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.4426 - acc: 0.8002\n",
      "Epoch 279/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.4438 - acc: 0.8070\n",
      "Epoch 280/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.4442 - acc: 0.8047\n",
      "Epoch 281/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.4426 - acc: 0.8002\n",
      "Epoch 282/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4429 - acc: 0.8047\n",
      "Epoch 283/300\n",
      "891/891 [==============================] - 0s 26us/step - loss: 0.4428 - acc: 0.8081\n",
      "Epoch 284/300\n",
      "891/891 [==============================] - 0s 28us/step - loss: 0.4430 - acc: 0.8047\n",
      "Epoch 285/300\n",
      "891/891 [==============================] - 0s 32us/step - loss: 0.4426 - acc: 0.8036\n",
      "Epoch 286/300\n",
      "891/891 [==============================] - 0s 40us/step - loss: 0.4424 - acc: 0.8047\n",
      "Epoch 287/300\n",
      "891/891 [==============================] - 0s 40us/step - loss: 0.4426 - acc: 0.8070\n",
      "Epoch 288/300\n",
      "891/891 [==============================] - 0s 35us/step - loss: 0.4425 - acc: 0.8081\n",
      "Epoch 289/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.4441 - acc: 0.8036\n",
      "Epoch 290/300\n",
      "891/891 [==============================] - 0s 36us/step - loss: 0.4424 - acc: 0.8036\n",
      "Epoch 291/300\n",
      "891/891 [==============================] - 0s 36us/step - loss: 0.4428 - acc: 0.8047\n",
      "Epoch 292/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.4426 - acc: 0.8070\n",
      "Epoch 293/300\n",
      "891/891 [==============================] - 0s 34us/step - loss: 0.4427 - acc: 0.8036\n",
      "Epoch 294/300\n",
      "891/891 [==============================] - 0s 31us/step - loss: 0.4426 - acc: 0.8058\n",
      "Epoch 295/300\n",
      "891/891 [==============================] - 0s 35us/step - loss: 0.4426 - acc: 0.8036\n",
      "Epoch 296/300\n",
      "891/891 [==============================] - 0s 30us/step - loss: 0.4431 - acc: 0.8047\n",
      "Epoch 297/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4442 - acc: 0.8058\n",
      "Epoch 298/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4442 - acc: 0.8036\n",
      "Epoch 299/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4424 - acc: 0.8058\n",
      "Epoch 300/300\n",
      "891/891 [==============================] - 0s 27us/step - loss: 0.4418 - acc: 0.8058\n"
     ]
    }
   ],
   "source": [
    "res = model.fit(x,y,epochs=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24ab613c2b0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dUxUT4eLzy3K56ix23oWa068xaOD3j3gKD7qPAeDTiyeTFBPhjFgaKhHpc5qQ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\nGviVUmqc0cCvlFLjzP8H02tlj36StQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24ab5d58b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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